[jira] [Updated] (KUDU-2483) Scan tablets with bloom filter

["jin xing (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/KUDU-2483?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

jin xing updated KUDU-2483:
---------------------------
    Attachment: image-2018-07-01-23-28-28-123.png

> Scan tablets with bloom filter
> ------------------------------
>
>                 Key: KUDU-2483
>                 URL: https://issues.apache.org/jira/browse/KUDU-2483
>             Project: Kudu
>          Issue Type: New Feature
>          Components: client
>            Reporter: jin xing
>            Priority: Major
>         Attachments: KUDU-2483, image-2018-07-01-23-28-28-123.png
>
>
> Join is really common/popular in Spark SQL, in this JIRA I take broadcast join as an example and describe how Kudu's bloom filter can help accelerate distributed computing.
> Spark runs broadcast join with below steps:
> 1. When do broadcast join, we have a small table and a big table; Spark will read all data from small table to one worker and build a hash table;
> 2. The generated hash table from step 1 is broadcasted to all the workers, which will read the splits from big table;
> 3. Workers start fetching and iterating all the splits of big table and see if the joining keys exists in the hash table; Only matched joining keys is retained.
> From above, step 3 is the heaviest, especially when the worker and split storage is not on the same host and bandwith is limited. Actually the cost brought by step 3 is not always necessary. Think about below scenario:
> {code:none}
> Small table A
> id      name
> 1      Jin
> 6      Xing
> Big table B
> id     age
> 1      10
> 2      21
> 3      33
> 4      65
> 5      32
> 6      23
> 7      18
> 8      20
> 9      22
> {code}
> Run query with SQL: *select * from A inner join B on A.id=B.id*
> It's pretty straight that we don't need to fetch all the data from Table B, because the number of matched keys is really small;
> I propose to use small table to build a bloom filter(BF) and use the generated BF as a predicate/filter to fetch data from big table, thus:
> 1. Much traffic/bandwith is saved.
> 2. Less data to processe by worker
> Broadcast join is just an example, other types of join will also benefit if we scan with a BF
> In a nutshell, I think Kudu can provide an iterface, by which user can scan data with bloom filters



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